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1.  Design and evaluation of a software for the objective and easy-to-read presentation of new drug properties to physicians 
Background
When new pharmaceutical products appear on the market, physicians need to know whether they are likely to be useful in their practices. Physicians currently obtain most of their information about the market release and properties of new drugs from pharmaceutical industry representatives. However, the official information contained in the summary of product characteristics (SPCs) and evaluation reports from health agencies, provide a more complete view of the potential value of new drugs, although they can be long and difficult to read. The main objective of this work was to design a prototype computer program to facilitate the objective appraisal of the potential value of a new pharmaceutical product by physicians. This prototype is based on the modeling of pharmaceutical innovations described in a previous paper.
Methods
The interface was designed to allow physicians to develop a rapid understanding of the value of a new drug for their practices. We selected five new pharmaceutical products, to illustrate the function of this prototype. We considered only the texts supplied by national or international drug agencies at the time of market release. The perceived usability of the prototype was evaluated qualitatively, except for the System Usability Scale (SUS) score evaluation, by 10 physicians differing in age and medical background.
Results
The display is based on the various axes of the conceptual model of pharmaceutical innovations. The user can select three levels of detail when consulting this information (highly synthetic, synthetic and detailed). Tables provide a comparison of the properties of the new pharmaceutical product with those of existing drugs, if available for the same indication, in terms of efficacy, safety and ease of use.
The interface was highly appreciated by evaluators, who found it easy to understand and suggested no other additions of important, internationally valid information. The mean System Usability Scale score for the 10 physicians was 82, corresponding to a “good” user interface.
Conclusions
This work led us to propose the selection, grouping, and mode of presentation for various types of knowledge on pharmaceutical innovations in a way that was appreciated by evaluators. It provides physicians with readily accessible objective information about new drugs.
Electronic supplementary material
The online version of this article (doi:10.1186/s12911-015-0158-2) contains supplementary material, which is available to authorized users.
doi:10.1186/s12911-015-0158-2
PMCID: PMC4460682  PMID: 26025025
2.  Improving access to clinical practice guidelines with an interactive graphical interface using an iconic language 
Background
Clinical practice guidelines are useful for physicians, and guidelines are available on the Internet from various websites such as Vidal Recos. However, these guidelines are long and difficult to read, especially during consultation. Similar difficulties have been encountered with drug summaries of product characteristics. In a previous work, we have proposed an iconic language (called VCM, for Visualization of Concepts in Medicine) for representing patient conditions, treatments and laboratory tests, and we have used these icons to design a user interface that graphically indexes summaries of product characteristics. In the current study, our objective was to design and evaluate an iconic user interface for the consultation of clinical practice guidelines by physicians.
Methods
Focus groups of physicians were set up to identify the difficulties encountered when reading guidelines. Icons were integrated into Vidal Recos, taking human factors into account. The resulting interface includes a graphical summary and an iconic indexation of the guideline. The new interface was evaluated. We compared the response times and the number of errors recorded when physicians answered questions about two clinical scenarios using the interactive iconic interface or a textual interface. Users’ perceived usability was evaluated with the System Usability Scale.
Results
The main difficulties encountered by physicians when reading guidelines were obtaining an overview and finding recommendations for patients corresponding to “particular cases”. We designed a graphical interface for guideline consultation, using icons to identify particular cases and providing a graphical summary of the icons organized by anatomy and etiology. The evaluation showed that physicians gave clinical responses more rapidly with the iconic interface than the textual interface (25.2 seconds versus 45.6, p < 0.05). The physicians appreciated the new interface, and the System Usability Scale score value was 75 (between good and excellent).
Conclusion
An interactive iconic interface can provide physicians with an overview of clinical practice guidelines, and can decrease the time required to access the content of such guidelines.
doi:10.1186/1472-6947-14-77
PMCID: PMC4153004  PMID: 25158762
Practice guidelines as topic; User-computer interface; Computer graphics; Iconic language
3.  Evaluating alignment quality between iconic language and reference terminologies using similarity metrics 
Background
Visualization of Concepts in Medicine (VCM) is a compositional iconic language that aims to ease information retrieval in Electronic Health Records (EHR), clinical guidelines or other medical documents. Using VCM language in medical applications requires alignment with medical reference terminologies. Alignment from Medical Subject Headings (MeSH) thesaurus and International Classification of Diseases – tenth revision (ICD10) to VCM are presented here. This study aim was to evaluate alignment quality between VCM and other terminologies using different measures of inter-alignment agreement before integration in EHR.
Methods
For medical literature retrieval purposes and EHR browsing, the MeSH thesaurus and the ICD10, both organized hierarchically, were aligned to VCM language. Some MeSH to VCM alignments were performed automatically but others were performed manually and validated. ICD10 to VCM alignment was entirely manually performed. Inter-alignment agreement was assessed on ICD10 codes and MeSH descriptors, sharing the same Concept Unique Identifiers in the Unified Medical Language System (UMLS). Three metrics were used to compare two VCM icons: binary comparison, crude Dice Similarity Coefficient (DSCcrude), and semantic Dice Similarity Coefficient (DSCsemantic), based on Lin similarity. An analysis of discrepancies was performed.
Results
MeSH to VCM alignment resulted in 10,783 relations: 1,830 of which were manually performed and 8,953 were automatically inherited. ICD10 to VCM alignment led to 19,852 relations. UMLS gathered 1,887 alignments between ICD10 and MeSH. Only 1,606 of them were used for this study. Inter-alignment agreement using only validated MeSH to VCM alignment was 74.2% [70.5-78.0]CI95%, DSCcrude was 0.93 [0.91-0.94]CI95%, and DSCsemantic was 0.96 [0.95-0.96]CI95%. Discrepancy analysis revealed that even if two thirds of errors came from the reviewers, UMLS was nevertheless responsible for one third.
Conclusions
This study has shown strong overall inter-alignment agreement between MeSH to VCM and ICD10 to VCM manual alignments. VCM icons have now been integrated into a guideline search engine (http://www.cismef.org) and a health terminologies portal (http://www.hetop.eu).
doi:10.1186/1472-6947-14-17
PMCID: PMC4007774  PMID: 24618037
Terminology as topic; International classification of diseases; Medical subject headings; Vocabulary; Controlled; Alignment; Iconic language; Compositional language; Semantic distances; Inter-alignment agreement
4.  Designing concept maps for a precise and objective description of pharmaceutical innovations 
Background
When a new drug is launched onto the market, information about the new manufactured product is contained in its monograph and evaluation report published by national drug agencies. Health professionals need to be able to determine rapidly and easily whether the new manufactured product is potentially useful for their practice. There is therefore a need to identify the best way to group together and visualize the main items of information describing the nature and potential impact of the new drug. The objective of this study was to identify these items of information and to bring them together in a model that could serve as the standard for presenting the main features of new manufactured product.
Methods
We developed a preliminary conceptual model of pharmaceutical innovations, based on the knowledge of the authors. We then refined this model, using a random sample of 40 new manufactured drugs recently approved by the national drug regulatory authorities in France and covering a broad spectrum of innovations and therapeutic areas. Finally, we used another sample of 20 new manufactured drugs to determine whether the model was sufficiently comprehensive.
Results
The results of our modeling led to three sub models described as conceptual maps representingi) the medical context for use of the new drug (indications, type of effect, therapeutical arsenal for the same indications), ii) the nature of the novelty of the new drug (new molecule, new mechanism of action, new combination, new dosage, etc.), and iii) the impact of the drug in terms of efficacy, safety and ease of use, compared with other drugs with the same indications.
Conclusions
Our model can help to standardize information about new drugs released onto the market. It is potentially useful to the pharmaceutical industry, medical journals, editors of drug databases and medical software, and national or international drug regulation agencies, as a means of describing the main properties of new pharmaceutical products. It could also used as a guide for the writing of comprehensive and objective texts summarizing the nature and interest of new manufactured product.
doi:10.1186/1472-6947-13-10
PMCID: PMC3560234  PMID: 23331768
5.  How to translate therapeutic recommendations in clinical practice guidelines into rules for critiquing physician prescriptions? Methods and application to five guidelines 
Background
Clinical practice guidelines give recommendations about what to do in various medical situations, including therapeutical recommendations for drug prescription. An effective way to computerize these recommendations is to design critiquing decision support systems, i.e. systems that criticize the physician's prescription when it does not conform to the guidelines. These systems are commonly based on a list of "if conditions then criticism" rules. However, writing these rules from the guidelines is not a trivial task. The objective of this article is to propose methods that (1) simplify the implementation of guidelines' therapeutical recommendations in critiquing systems by automatically translating structured therapeutical recommendations into a list of "if conditions then criticize" rules, and (2) can generate an appropriate textual label to explain to the physician why his/her prescription is not recommended.
Methods
We worked on the therapeutic recommendations in five clinical practice guidelines concerning chronic diseases related to the management of cardiovascular risk. We evaluated the system using a test base of more than 2000 cases.
Results
Algorithms for automatically translating therapeutical recommendations into "if conditions then criticize" rules are presented. Eight generic recommendations are also proposed; they are guideline-independent, and can be used as default behaviour for handling various situations that are usually implicit in the guidelines, such as decreasing the dose of a poorly tolerated drug. Finally, we provide models and methods for generating a human-readable textual critique. The system was successfully evaluated on the test base.
Conclusion
We show that it is possible to criticize physicians' prescriptions starting from a structured clinical guideline, and to provide clear explanations. We are now planning a randomized clinical trial to evaluate the impact of the system on practices.
doi:10.1186/1472-6947-10-31
PMCID: PMC2893080  PMID: 20509903
6.  Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes 
Background
Clinical guidelines carry medical evidence to the point of practice. As evidence is not always available, many guidelines do not provide recommendations for all clinical situations encountered in practice. We propose an approach for identifying knowledge gaps in guidelines and for exploring physicians' therapeutic decisions with data mining techniques to fill these knowledge gaps. We demonstrate our method by an example in the domain of type 2 diabetes.
Methods
We analyzed the French national guidelines for the management of type 2 diabetes to identify clinical conditions that are not covered or those for which the guidelines do not provide recommendations. We extracted patient records corresponding to each clinical condition from a database of type 2 diabetic patients treated at Avicenne University Hospital of Bobigny, France. We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm. We developed decision-trees for different levels of detail of the therapeutic decision, namely the type of treatment, the pharmaco-therapeutic class, the international non proprietary name, and the dose of each medication. We compared the rules generated with those added to the guidelines in a newer version, to examine their similarity.
Results
We extracted 27 rules from the analysis of a database of 463 patient records. Eleven rules were about the choice of the type of treatment and thirteen rules about the choice of the pharmaco-therapeutic class of each drug. For the choice of the international non proprietary name and the dose, we could extract only a few rules because the number of patient records was too low for these factors. The extracted rules showed similarities with those added to the newer version of the guidelines.
Conclusion
Our method showed its usefulness for completing guidelines recommendations with rules learnt automatically from physicians' prescriptions. It could be used during the development of guidelines as a complementary source from practice-based knowledge. It can also be used as an evaluation tool for comparing a physician's therapeutic decisions with those recommended by a given set of clinical guidelines. The example we described showed that physician practice was in some ways ahead of the guideline.
doi:10.1186/1472-6947-9-28
PMCID: PMC2700100  PMID: 19515252
7.  A novel method for measuring patients' adherence to insulin dosing guidelines: introducing indicators of adherence 
Background
Diabetic type 1 patients are often advised to use dose adjustment guidelines to calculate their doses of insulin. Conventional methods of measuring patients' adherence are not applicable to these cases, because insulin doses are not determined in advance. We propose a method and a number of indicators to measure patients' conformance to these insulin dosing guidelines.
Methods
We used a database of logbooks of type 1 diabetic patients who participated in a summer camp. Patients used a guideline to calculate the doses of insulin lispro and glargine four times a day, and registered their injected doses in the database. We implemented the guideline in a computer system to calculate recommended doses. We then compared injected and recommended doses by using five indicators that we designed for this purpose: absolute agreement (AA): the two doses are the same; relative agreement (RA): there is a slight difference between them; extreme disagreement (ED): the administered and recommended doses are merely opposite; Under-treatment (UT) and over-treatment (OT): the injected dose is not enough or too high, respectively. We used weighted linear regression model to study the evolution of these indicators over time.
Results
We analyzed 1656 insulin doses injected by 28 patients during a three weeks camp. Overall indicator rates were AA = 45%, RA = 30%, ED = 2%, UT = 26% and OT = 30%. The highest rate of absolute agreement is obtained for insulin glargine (AA = 70%). One patient with alarming behavior (AA = 29%, RA = 24% and ED = 8%) was detected. The monitoring of these indicators over time revealed a crescendo curve of adherence rate which fitted well in a weighted linear model (slope = 0.85, significance = 0.002). This shows an improvement in the quality of therapeutic decision-making of patients during the camp.
Conclusion
Our method allowed the measurement of patients' adherence to their insulin adjustment guidelines. The indicators that we introduced were capable of providing quantitative data on the quality of patients' decision-making for the studied population as a whole, for each individual patient, for all injections, and for each time of injection separately. They can be implemented in monitoring systems to detect non-adherent patients.
doi:10.1186/1472-6947-8-55
PMCID: PMC2636792  PMID: 19061492
8.  Design of a graphical and interactive interface for facilitating access to drug contraindications, cautions for use, interactions and adverse effects 
Background
Drug iatrogeny is important but could be decreased if contraindications, cautions for use, drug interactions and adverse effects of drugs described in drug monographs were taken into account. However, the physician's time is limited during consultations, and this information is often not consulted. We describe here the design of "Mister VCM", a graphical interface based on the VCM graphical language, facilitating access to drug monographs. We also provide an assessment of the usability of this interface.
Methods
The "Mister VCM" interface was designed by dividing the screen into two parts: a graphical interactive one including VCM icons and synthetizing drug properties, a textual one presenting on demand drug monograph excerpts. The interface was evaluated over 11 volunteer general practitioners, trained in the use of "Mister VCM". They were asked to answer clinical questions related to fictitious randomly generated drug monographs, using a textual interface or "Mister VCM". When answering the questions, correctness of the responses and response time were recorded.
Results
"Mister VCM" is an interactive interface that displays VCM icons organized around an anatomical diagram of the human body with additional mental, etiological and physiological areas. Textual excerpts of the drug monograph can be displayed by clicking on the VCM icons. The interface can explicitly represent information implicit in the drug monograph, such as the absence of a given contraindication. Physicians made fewer errors with "Mister VCM" than with text (factor of 1.7; p = 0.034) and responded to questions 2.2 times faster (p < 0.001). The time gain with "Mister VCM" was greater for long monographs and questions with implicit replies.
Conclusion
"Mister VCM" seems to be a promising interface for accessing drug monographs. Similar interfaces could be developed for other medical domains, such as electronic patient records.
doi:10.1186/1472-6947-8-21
PMCID: PMC2442832  PMID: 18518945
9.  An iconic language for the graphical representation of medical concepts 
Background
Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.
Methods
The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.
Results
VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, p = 0.003) and 1.8 times faster (p < 0.001).
Conclusion
VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.
doi:10.1186/1472-6947-8-16
PMCID: PMC2413217  PMID: 18435838

Results 1-9 (9)